SonarQube
SonarQube doesn't run your tests — it reads coverage reports that your test runner produces. To see coverage in SonarQube, you need two things: a test run that generates a coverage file (LCOV, JaCoCo XML, Cobertura XML, etc.) and a scanner configuration that tells SonarQube where to find it.
Rust
Code coverage tells you which lines your tests exercise. It won't tell you if your tests are good, but it does tell you what you're definitely not testing. In Rust, you have two main options: cargo-tarpaulin and cargo-llvm-cov. This guide covers both, when
Coverage.py
Coverage.py measures which Python lines, branches, and functions are executed during tests. Use it with pytest-cov for seamless pytest integration, enforce minimum coverage with --fail-under, and generate XML reports for Codecov, SonarQube, and GitHub Actions integrations. Coverage.py is the standard Python coverage measurement tool. Unlike language-
Code Coverage
Coverage gates block pull request merges when new code isn't adequately tested. Set them up using Codecov, SonarQube, or custom scripts in GitHub Actions. The key is enforcing patch coverage (new code) rather than project coverage (overall), which prevents penalizing teams for pre-existing gaps. Key Takeaways Enforce
Code Coverage
Coverage percentage is a proxy metric. Like most proxy metrics, it becomes counterproductive the moment teams start optimizing for it directly. The problem isn't coverage tools. The problem is treating a measurement of test quantity as a measurement of test quality. You can achieve 100% line coverage with
Codecov
Codecov's codecov.yml file controls thresholds, PR comments, coverage flags, carryforward settings, and ignore patterns. Without it, Codecov uses defaults that may not match your project's standards. This guide covers the most useful settings. Key Takeaways codecov.yml lives at the root of your repository. Codecov
Codecov
Codecov and Coveralls both receive coverage reports from CI, display trends over time, and post PR comments showing coverage changes. Codecov offers more powerful PR analytics and supports more coverage formats. Coveralls is simpler and free for open source. This guide covers setting up both and integrating with GitHub. Running
Code Coverage
Effective CI coverage strategies enforce coverage on new code (patch coverage), gradually increase thresholds over time (ratchet), and fail builds only for meaningful drops—not arbitrary percentages. This guide covers threshold patterns, per-language implementations, and common anti-patterns to avoid. Coverage metrics are misused more often than used well.
Code Coverage
Coverage tools report four or five different metrics, and most teams pick one number to track without fully understanding what the others measure. That's a problem because each metric catches different categories of untested code, and conflating them gives you false confidence. This is the complete breakdown. Statement
Testing
Coverage thresholds block merges when code coverage drops below your team's standard. SonarQube enforces thresholds as quality gate conditions; Codecov enforces them via a codecov.yml file. This guide covers both approaches, how to set realistic thresholds, and how to increase them over time. Why Coverage Thresholds Matter
Testing Tools
Istanbul is the most widely used JavaScript code coverage tool. Its CLI wrapper nyc makes it trivial to add coverage instrumentation to any Node.js project.
Testing Tools
Codecov is a coverage tracking service that aggregates coverage reports from your CI runs, displays trends over time, and adds pull request comments showing coverage changes for every commit. This tutorial covers setup, configuration, PR integration, and coverage gates. Why Codecov Over Just Viewing Coverage Locally Running nyc report or